from clustergrammer2 import net
df = {}
import numpy as np
import pandas as pd
import gene_exp_10x
Load ADT data, arcsinh transform the ADT levels, then Z-score ADT levels across cells.
df['adt-ini'] = pd.read_csv('../data/big_data/CITE-seq_CBMC_8K_13AB_10X/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv', index_col=0)
df['adt'] = np.arcsinh(df['adt-ini']/5)
net.load_df(df['adt'])
net.normalize(axis='row', norm_type='zscore')
df['adt-z'] = net.export_df()
df['adt'].shape
net.load_df(df['adt-z'])
net.widget()